Abstract
Bayesian networks and undirected graphical models are often used to cope with uncertainty for complex systems with a large number of variables. They can be applied to discover causal relationships and associations between variables. In this paper, we present heuristic algorithms for structural learning of undirected graphical models from observed data. These algorithms are applied to traditional Chinese medicine.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cowell, R.G., David, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer Publications, New York (1999)
Geng, Z., Wang, C., Zhao, Q.: Decompsition of search for v-structures in DAGs. J. Multivar. Analy. (2004) (to appear)
Heckerman, D.: A tutorial on learning with Bayesian networks. In: Jordan, M.I. (ed.) Learning in Graphical Models, pp. 301–354. Kluwer Academic Pub, Netherlands (1998)
Lauritzen, S.L.: Graphical models. Oxford University Press, Oxford (1996)
Pearl, J.: Causality. Cambridge University Press, Cambridge (2000)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search, 2nd edn. MIT Press, Cambridge (2000)
Verma, T., Pearl, J.: Equivalence and synthesis of causal models. In: Bonissone, P., Henrion, M., Kanal, L.N., Lemmer, J.F. (eds.) Uncertainty in Artificial Intelligence, vol. 6, pp. 255–268. Elsevier, Amsterdam (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Deng, K., Liu, D., Gao, S., Geng, Z. (2005). Structural Learning of Graphical Models and Its Applications to Traditional Chinese Medicine. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_45
Download citation
DOI: https://doi.org/10.1007/11540007_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
eBook Packages: Computer ScienceComputer Science (R0)